name: ai_article_research description: "Research the article series topic — live search + synthesis + spawn article plan task." debug: true rag_exclude: [business_plan, charter] system: agent_prompt agent_prompt: - "= identity.md" - "agent.rag.json" sections: - agent - project - history - rag - prior_results - message - instructions builders: prior_results: | *** WEB SEARCH RESULTS *** {steps[1].text} (If the above is empty, use your expert training knowledge.) steps: - type: think hint: | Read the project description and the message above carefully. Identify the best search query to find current, real-world information on this topic. State your reasoning, then on the last line write: SEARCH QUERY: [your query here] Query rules: 3-8 words. Specific. Current year preferred. - type: tool capability: Tool_WebSearcher input_from: last_text - type: think hint: | Using the search results above (or your training knowledge if unavailable), write a RESEARCH BRIEF on the topic from the project description. Cover as many relevant angles and subtopics as you can find real evidence for. For each angle: what is happening, what problem it solves, one concrete result. End with a SERIES RECOMMENDATION section proposing exactly 10 article topics. For each topic: working title, target reader, the one thing they will learn. - type: document filename: "{{task_name_slug}}" - type: spawn task_type: ai_article_plan task_name: "Plan Article Series: {project.name}" message: "{task.message}" - type: close rag_update: true adjudication: enabled: true pass_threshold: 65 deliverable_type: coordination criteria: accuracy: weight: 35 description: "Facts are correct and verifiable" thoroughness: weight: 30 description: "Topic covered in sufficient depth" source_quality: weight: 20 description: "Sources are credible and relevant" organization: weight: 15 description: "Findings are well-structured"